In this article we provide a fresh perspective on how one can improve credit risk assessment in lending to help reduce non-performing assets (NPAs). The benefits of improving credit risk assessment are immense for all of us as a society, more details on which can be found here. We notice that there are some fundamental changes required in how one uses and builds credit scores to improve the overall risk assessment. Throughout the article, we consider the case of lending to individuals as against businesses/institutions, however, we extend our suggestions to the case of lending to businesses at the end.

We begin with explaining the distinction between credit scores (as they exist today in most parts of the world) and credit risk assessment estimates. This we believe is a source of major confusion leading to several sub-optimal credit/lending decisions all over the world, both at the retail and institutional levels. We propose a simple yet extremely effective approach towards credit risk decision making via two quantitative signals, one that provides a view of the past and another which provides an estimate of the future. The delinking (of the past and the future) while may not be necessary mathematically for making a good decision, plays an important role in addressing the behavioural loopholes that persist.

A credit score is traditionally expected to be indicative of whether a person is creditworthy or not, potentially to extend a new credit line in the future. At the same time, it is also expected to be indicative of how well the person has performed on the credit lines extended to her in the past. This presents a contradiction in expectations since a person with good credit history may not necessarily be automatically qualified for a new credit line. Assuming that a person with good credit history will continue to be good with any future credit lines would be a classic example of an error like “hot hand fallacy”introduced by Amos Tversky, Thomas Gilovich, and Robert Vallone (1985) and discussed later by Prof. Richard Thaler, the 2017 Nobel prize winner for Economics (although the concept was originally analysed w.r.t. random sequences and basketball players). For instance, if someone is paying a number of dues/instalments on past loans extended to her with minimal savings left, then it would be risky to extend a new loan to her even though her credit history may be very good. (Indeed, many successful lending institutions do this due diligence beyond the credit scores offered by various bureaus, however, there is no clear regulatory obligation to do the same which we recommend for the future).

In reality, most credit scores which are available today end up becoming a mix of sorts of a person’s past credit behaviour and her future creditworthiness without doing proper justice to either of them. Figure 1 shows the current state of various credit scoring models. For instance, guidelines like, the more the number of credit lines you have the better score you would have, the more the credit line utility one has the better the score will be, have become rules that everyone is getting forced to adjust to, even though such rules do not make any sound logical sense except for the people inside the industry building those models/rules who are stretched with requirements between the two sides of the axis shown in Figure 1. We must move towards a better system where people are not forced to take up more credit cards or adjust their spending patterns just for the sake of getting high credit scores even though they might be paying all their dues on time. And anyone who pays their dues on time on all their credit lines, even if it’s just a single credit line, should get a high CHO score.

Figure 1: Representation of various current credit scoring models on an axis that extends from a CHO model which is purely representing past credit behaviour towards a CRA model which is purely representing future credit risk assessment.

In the following, we will discuss why having two distinct scores one representative of a person’s credit history and the other representative of the person’s future credit risk is beneficial in many ways (than any of the extremes or a single combination as we see today). The reasons go beyond mathematics and touch upon the various use-cases where such scores are used and the human behaviour itself. Also, while the CHO score can be updated regularly and enquired for any number of times without effecting that score itself (which actually happens with many current credit scoring models—the number of enquiries effects the score inversely), the CRA score can just be computed only when a user applies for a particular loan and can include the parameters from her application form and more as of that time.

Here, one may be tempted to suggest—why not completely let go of the credit history-based scores and only have a score corresponding to one’s future creditworthiness. One objection from customers would then be that, if they paid all their dues on time, why are they being represented with a potentially low score. Even though one may try to explain about the way it is measured, i.e., estimating for the future, one may face hard time convincing people. A stronger objection though, is that the credit scores which are representative of the past have their own utility in various places. For instance, when one is moving from one rental home to another, or when someone is looking for new utilities connections, etc., many cases where there is no large credit line being extended, where there is no critical necessity for doing a more complex and expensive credit risk assessment, the score which is representative of a person’s credit history more than serves the purpose.

However, as discussed before, using a score which is representative of the past for extending new credit lines into the future would not be sound. As many successful lending institutions already follow, it is advisable to have a separate risk score which estimates one’s ability to repay future loans extended to her. Even with the institutions which have such a mechanism in place, the motivation to develop such scores didn’t necessarily stem from the above observations, rather it stems from the observation that the scores available from various bureaus were not performing well (or doing very badly in some cases) in estimating NPAs upfront. In such cases, we hope that the above discussion helps understand why such scores were not performing well and cannot be expected to perform well for estimating future NPAs unless the above proposed distinction is implemented.

Let us take a more detailed look at each of the four cases that arise with having two scores:

High CHO score and high CRA score. A person has a good credit history and also has many other parameters in right place to support a good future credit extension as well.

High CHO score and low CRA score. A person has a good credit history, however, the support for a new credit line extension is extremely weak. This case would generate a lot of confusion with current credit scoring models forcing them to give a score somewhere in between depending on which part of the axis they fall into as in Figure 1. Such a confusion neither serves use-cases like getting new rental homes or utility connections well (which should ideally be straightforward for such a person) nor serve a purpose like getting a new loan (which should not be extended ideally, whereas some current models might suggest otherwise).

Low CHO score and high CRA score. A person who has missed on her payments in the past (maybe a while ago) might end up with a low CHO score (depending on the model), while her current parameters may reflect a high CRA score. We believe though that this possibility is less likely since good CHO models would already try to incorporate the intensity of credit defaults and the timing of them, as would good CRA models which usually have correlations with such parameters from the past. (This type of cases if observed in large numbers may mean either the CHO model or the CRA model being used (or both) is faulty/needs to be improved.)

Low CHO score and Low CRA score. An individual who has credit defaults in the past and no indicators showcasing support otherwise for the future either.

We hope that regulators note this distinction and consider enforcing lenders to measure and note down quantitatively, two distinct metrics about each applicant/application, one that is representative of the past credit behaviour and one that estimates the future creditworthiness. A low future score should definitely act as a warning signal for any new credit line extension.

Before we conclude, we also wish to recommend similar adoption of two distinct scores even for lending at an MSME/big business/institutional level, and the enforcement of the same by the regulatory authorities. This again can help avoid over optimism leading to higher NPAs, such as the ones described by Prof. Raghuram Rajan, Ex- RBI Governor, in his note to the parliamentary committee of India on NPAs—“One promoter told me about how he was pursued then by banks waving checkbooks, asking him to name the amount he wanted.” Such instances if not well-deserved, will automatically face the reality check when the lender tries to put the second quantitative estimate about the future potential of the candidate/application, especially knowing that the same would have to be explained in the future in case the asset turns NP. Just by enforcing such a metric forces people to think realistically about the future creditworthiness and the associated modelling instead of just the past (while also relieving the pressure of assigning a high score for the future just because someone might have a good credit history).

In conclusion, we argued that there is a need for two distinct scores one that is representative of a person’s past credit behaviour and another which estimates the person’s future creditworthiness both of which solve different sets of purposes and using a single combined score would fall sub-optimal in serving either of those sets of purposes. We hope that our suggestions appeal to the lending community, the regulatory authorities, as well as to the people in better understanding and harnessing the power of credit for a better world.

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Satya Gautam Vadlamudi is the Head (VP) of Data Science and Artificial Intelligence at CreditVidya. He is an accomplished Data Scientist with multiple projects under his belt. Gautam previously worked as the Lead Data Scientist for Capillary Technologies and has also worked at Google and was a critical team member for development of Orkut. He is an academician at heart and has completed his post Doctoral research from Arizona State University and completed his PhD from IIT Kharagpur. Gautam also worked at Intel Corporation in the past.